## Code that creates Figure where we don't match on pre-treatment outcomes
## Last changed: 2025-05-23


  sink("C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Output/FigureA7A8.txt", split=TRUE)

library(PSweight)
library(cobalt)
library(MatchIt)
library(distances)
library(moreparty)
library(party)
library(caret)
library(bonsai)
library(randomForest)
library(tidymodels)
library(tidyverse)
library(rmarkdown)
library(tinytex)
library(tibble)
library(knitr)
library(car)
library(fst)
library(stargazer)
library(pryr)
library(stringr)
library(fst)
library(lubridate)
library(data.table)
library(summarytools)
library(ggplot2)
library(cowplot)
library(ggpubr)
library(haven)
library(broom)
library(fixest)
library(modelsummary)
library(gt)
library(MASS)
library(survey)
library(collapse)
library(readxl)
library(Hmisc)
library(DescTools)
library(ranger)
library(stablelearner)
library(future.apply)
library(lme4)
library(partykit)
library(ggiplot)


# Specify the name of the treatment variable
  tvar <- "T94_90"
  
# Specify the the birth cohorts to be used for the analysis
  lbyr <- 1942
  ubyr <- 1967
  
# Specify if only include Swedish citizens 
  citizen <- 1
  
  # Specify the propensity score specification
  psformula <- formula(T94 ~
                         Kon + DAStod_1982 + DAStod_1983 + DAStod_1984 +
                         DAStod_1985 + DAStod_1986 + DAStod_1987 + DAStod_1988 +
                         DAStod_1989 +
                         DAntBarn_91 + Sambo_91 + InvBak  |
                         SSYK3_90 + SNI2_91 + Kommun_91 +
                         rCARB_1982 + rCARB_1983 + rCARB_1984 + rCARB_1985 +
                         rCARB_1986 + rCARB_1987 + rCARB_1988 + rCARB_1989 +
                         pArbInk_1990 + pArbInk_1991 + UtbAr_91 +
                         FodAr)
 
## Estimate two models, one for the complete sample including all observations,
## the second on the restricted sample only including individuals for which
## we can observe all elections bw 82-18 in which they are eligible to run for office. 

  Models <-  vector('list', 5)
  j <- 1

 for(i in 1:5) { 
  # Read in the data sets
  NomVald <- read_fst("E:/ProjData/Jan_Kalle/JoPReplications/DB_NomVald.fst", as.data.table=T)
  RTB <- read_fst("E:/ProjData/Jan_Kalle/JoPReplications/DB_RTB.fst", as.data.table=T)
  db <- read_fst("E:/ProjData/Jan_Kalle/JoPReplications/DB_Nom.fst", as.data.table=T)
 
  db[, T94 := get(tvar)]
  mdb <- db[between(FodAr, lbyr, ubyr) & (SvMedb82_18 >= citizen) & AllaVal==1,]
  rm(db)
  gc()
  

  for(k in 1982:1989) {
    mdb[get(paste0("CARB_", k)) > 0, paste0("rCARB_", k) := cut_number(get(paste0("CARB_", k)), 100, labels = FALSE)]
    mdb[is.na(get(paste0("rCARB_", k))), paste0("rCARB_", k) := 0]
  }
  
  # Split the in SES quartiles

  mdb[!is.na(avSES3), ravSES := cut_number(avSES3, 4, labels = FALSE)]
  print(mdb[, .N, by= ravSES])
  
  if(i==1) mdb <- mdb[between(ravSES, 1, 1)]
  if(i==2) mdb <- mdb[between(ravSES, 2, 2)]
  if(i==3) mdb <- mdb[between(ravSES, 3, 3)]
  if(i==4) mdb <- mdb[between(ravSES, 4, 4)]
  
# Use logit model to estimate propensity scores
  mdb <- mdb[complete.cases(mdb),]
  tm <- proc.time()
  logmod <- feglm(psformula,  
                data = mdb, family="binomial")
  proc.time()-tm
  print(summary(logmod))

# Extract predicted probability (propensity scores)
  if(logmod$nobs == logmod$nobs_origin){ 
    mdb[, pT94 := predict(logmod, type = "response")]
  }else{
    mdb[logmod$obs_selection$obsRemoved, pT94 := predict(logmod, type = "response")]
  }

  mdb <- mdb[complete.cases(mdb),]

# Now use the Full matching approach of Sävje et al. to obtain ATT and ATE weights.  

  tmp <- proc.time()
  set.seed(7892)
  m.out <- matchit(T94~1, data = mdb,
                 distance = mdb$pT94, method = "quick", estimand = "ATT")
  proc.time()-tmp
  m.data1 <- as.data.table(match.data(m.out))[, .(LopNr, weights)]
  setnames(m.data1, "weights", "gm_att")

  mdb <- m.data1[, .(LopNr, gm_att)][mdb, on = "LopNr"]
  gc()
  RTB <- mdb[RTB, on = "LopNr"]

  rm(mdb)
  gc()

  rm(list=setdiff(ls(), c("NomVald", "RTB", "rtbvar", "Models", "j", "tvar", "lbyr", "ubyr", "citizen", "psformula")))
  gc()


  mdb <- RTB[!is.na(gm_att), 
             .(Kon, aNom, Nom82, Nom85, Nom88, Nom91, Sysselsatt_85, DAntBarn_91, Sambo_91, 
               DStud_91, DForLed_91, DSjukRe_91, DSocBidrFam_91, DBostBidrFam_91, Kommun,
               UtbAr_91, FodAr, InvBak, isei, Yrke80_90, SNI2_90, Kommun_91, fp_LoneInk_85, fp_LoneInk_90, fp_LoneInk_91,
               gm_att, Nom, LopNr, T94, Ar, pT94)]

  gc()
  
  mdb[, Nom := Nom*100]
  
  rm(RTB)
  gc()

  Models[[j]] <- feols(Nom~T94+i(Ar, T94, ref=1991) | Ar, cluster ="LopNr", 
                       weights = mdb[, gm_att], 
                       mdb[])
  
  print(summary(Models[[j]]))
  print(uniqueN(mdb[T94==0, LopNr]))
  print(uniqueN(mdb[T94==1, LopNr]))
  print(uniqueN(mdb[, LopNr]))
  print(dim(mdb))
  j <- j+1
}  
  
  valar <- c(1982, 1985, 1988, 1991, 1994, 1998, 2002, 2006, 2010, 2014, 2018, 2022)
  options(scipen=999)
  
  for(i in 1:4) {
    
    gi <- ggiplot(Models[[i]]) 
    gp <- ggplot(gi$data, aes(x, estimate)) +
      geom_point() + 
      geom_hline(yintercept=0, linetype="longdash") +
      geom_errorbar(aes(ymin=ci_low,ymax=ci_high), width=0.4, size=.6) +
      geom_line(linetype="dotted") +
      scale_x_continuous(breaks = valar,  labels = valar, limits = c(1981, 2023)) +
      scale_y_continuous(limits = c(-.4, .9), breaks = round(seq(-.4, .9, by = .1), digits=1)) +
      xlab("Election Year") +
      ylab("Effect on Candidacy") +
      ggtitle(paste0("Quartile ", i, " SES")) +
      theme_classic(base_size = 12) +
      theme(legend.title=element_blank()) +
      theme(legend.position = "bottom", panel.grid.major.y = element_line(color = "grey95"))
      
    gp
    
    assign(paste0("gp", i), gp)
  }  
  
  ggarrange(gp1, gp2, gp3, gp4)
  ggsave("C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Figures/FigureA8.pdf", width = 15, height =10, units = "in") 
 
  for(i in 5:5) {
    
    gi <- ggiplot(Models[[i]]) 
    gp <- ggplot(gi$data, aes(x, estimate)) +
      geom_point() + 
      geom_hline(yintercept=0, linetype="longdash") +
      geom_errorbar(aes(ymin=ci_low,ymax=ci_high), width=0.4, size=.6) +
      geom_line(linetype="dotted") +
      scale_x_continuous(breaks = valar,  labels = valar, limits = c(1981, 2023)) +
      scale_y_continuous(limits = c(-.1, .3), breaks = round(seq(-.1, .3, by = .05), digits=1)) +
      xlab("Election Year") +
      ylab("Effect on Candidacy") +
      theme_classic(base_size = 12) +
      theme(legend.title=element_blank()) +
      theme(legend.position = "bottom", panel.grid.major.y = element_line(color = "grey95"))
    
    gp
    ggsave(paste0("C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Figures/FigureA7.pdf"), width = 15, height =10, units = "in") 
    
    assign(paste0("gp", i), gp)
  }  
  modelsummary(Models[c(5, 1, 2, 3, 4)], output = "C:/Userdata/Shared/Dofiles/DoAnalysis/JanKalle/ReplicationFiles/AnalysisScripts/Tables/TableFigA7A8.tex")
  
  sink()